Topic 1 Question 340
A tourism company uses a machine learning (ML) model to make recommendations to customers. The company uses an Amazon SageMaker environment and set hyperparameter tuning completion criteria to MaxNumberOfTrainingJobs.
An ML specialist wants to change the hyperparameter tuning completion criteria. The ML specialist wants to stop tuning immediately after an internal algorithm determines that tuning job is unlikely to improve more than 1% over the objective metric from the best training job.
Which completion criteria will meet this requirement?
MaxRuntimeInSeconds
TargetObjectiveMetricValue
CompleteOnConvergence
MaxNumberOfTrainingJobsNotImproving
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コメント(3)
- 正解だと思う選択肢: C
onvergence detection is a completion criteria that lets automatic model tuning decide when to stop tuning. Generally, automatic model tuning will stop tuning when it estimates that no significant improvement can be achieved.
👍 2georgejinh2024/08/30 - 正解だと思う選択肢: C
C is the correct answer .CompleteOnConvergence – A flag to stop tuning after an internal algorithm determines that the tuning job is unlikely to improve more than 1% over the objective metric from the best training job. https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-progress.html
👍 2Tkhan12024/09/18 - 正解だと思う選択肢: C
The completion criteria that will meet this requirement is CompleteOnConvergence. This criterion stops the tuning job immediately after an internal algorithm determines that the tuning job is unlikely to improve more than 1% over the objective metric from the best training job
👍 1MultiCloudIronMan2024/09/23
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